CAMS products for analyzing atmospheric dynamics to develop QA indicators at regional scale

CAMS products for analyzing atmospheric dynamics to develop QA indicators at regional scale | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article CAMS products for analyzing atmospheric dynamics to develop QA indicators at regional scale Sarah Marion, Nadège Martiny, Pascal Roucou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7564554/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Air pollution episodes involving fine particulate matter (PM₂.₅) are tightly linked to synoptic meteorology, which regulates accumulation and dispersion. This study evaluates the ability of Copernicus Atmosphere Monitoring Service (CAMS) reanalyses (2015–2023) to support a daily-scale classification of circulation regimes relevant for air quality in eastern France. CAMS near-surface parameters (temperature, relative humidity, wind) were compared with the high-resolution SAFRAN reanalyses, and CAMS sea-level pressure fields were used to derive a reproducible classification benchmarked against Großwetterlagen. The present study highlights three main regimes. Anticyclonic situations promote strong PM₂.₅ accumulation under stable, poorly ventilated conditions. Low-pressure regimes enhance dispersion through stronger winds and mixing, limiting concentrations. An intermediate regime, less documented in previous classifications, combines moderate pressure gradients and variable transport pathways, producing heterogeneous pollution levels and occasional long-range particle transport. Results show good climatological agreement between CAMS and SAFRAN, with CAMS reproduces the main meteorological and synoptic patterns, while smoothing finer-scale contrasts. The classification explains both seasonal patterns and interannual variability, while underlining the persistent contribution of local emissions (traffic, heating, industry). Overall, CAMS provides a robust synoptic-scale framework for meteorological typologies relevant to air quality. Although its coarse resolution constrains intra-urban representation, coupling with high-resolution urban models could substantially enhance the diagnosis, forecasting, and management of particulate pollution episodes. This approach would not only improve the characterization of wintertime events but also capture the broader annual particle season, thereby providing more robust support for the development of effective mitigation strategies. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Particulate matter (PM) is commonly classified by aerodynamic diameter into PM₁₀, PM₂.₅, and PM₁, with smaller particles penetrating deeper into the respiratory system. It therefore represents a major public health concern due to its association with respiratory and cardiovascular diseases (Brunekreef et al., 2021 ; J. Chen & Hoek, 2020 ; Fiordelisi et al., 2017 ). In Europe, air quality is monitored through regulatory networks providing surface concentrations, with thresholds defined by European legislation (PM₁₀: 50 µg/m³ for 24 h, 40 µg/m³ annual; PM₂.₅: 25 µg/m³ annual) and increasingly guided by stricter World Health Organization (WHO) recommendations (PM₁₀: 45 µg/m³ 24 h, 15 µg/m³ annual; PM₂.₅: 15 µg/m³ 24 h, 5 µg/m³ annual). Urban PM concentrations result from a complex interplay of emission sources – natural (e.g., desert dust, wildfires; (Artı́ñano et al., 2003 )) and anthropogenic (e.g., residential heating, traffic; (Kassomenos et al., 2014 )) – and meteorological conditions (wind, temperature inversions, humidity) that govern dispersion, accumulation, and wet deposition (Bodor et al., 2020 ). Long-range transport can sustain elevated pollution over several days even in the absence of strong local emissions (Salvador et al., 2008 ; Waked et al., 2014 ). Topography amplifies these effects, particularly in valleys during winter anticyclonic conditions. Despite numerous studies linking meteorology and PM concentrations, few have addressed their combined spatiotemporal variability in urban contexts (Z. Chen et al., 2020 ). For instance, Yang et al., ( 2017 ) explored relationships between PM₂.₅ and meteorological factors across 74 major Chinese cities over nearly two years, revealing that relative humidity correlates positively with PM₂.₅ in northern regions but negatively elsewhere, while wind speed uniformly shows a negative correlation except over Hainan; correlations with temperature and pressure also vary seasonally and regionally. Additionally, Huszar et al., ( 2024 ) quantified how urbanization in central Europe modifies PM₂.₅ concentrations, disentangling contributions from urban canopy meteorological forcing and altered deposition and emission patterns, and found that urban-specific meteorological changes can slightly reduce PM₂.₅ while emissions dominate increases Previous work in the north-eastern French region of Bourgogne Franche Comté (Marion et al., 2025 ) highlighted the central role of atmospheric circulation in shaping PM patterns, using high-resolution SAFRAN data (8 km) combined with Großwetterlagen (GWL) classifications to capture both spatial and temporal dynamics. Building on this, the present study evaluates the Copernicus Atmosphere Monitoring Service (CAMS) as a multi-scale tool for diagnosing PM₂.₅ pollution. CAMS provides a consistent and complete database that aims to establish a replicable method and approach. Despite its coarser resolution (~ 80 km), CAMS provides consistent, high-frequency meteorological and aerosol data across Europe, making it suitable for synoptic-scale analyses. This study addresses two main research questions: to what extent can surface pressure fields and circulation regimes derived from CAMS reanalysis reproduce regional-scale atmospheric structures previously identified with higher-resolution datasets? Can CAMS-based synoptic regimes – particularly anticyclonic blocking and intermediate situations – explain the observed variability of daily and seasonal PM₂.₅ concentrations, and thus serve as indicators for anticipating winter pollution episodes? More generally, can CAMS-derived circulation regimes and the associated methodology be transferred to different geographical areas and larger spatial scales, enabling broader applications in air quality management? The primary objective is to evaluate whether CAMS can support the development of a daily-scale, meteorologically driven classification of atmospheric situations relevant to PM₂.₅ accumulation and dispersion. Specifically, we aim to: (i) assess the climatological characterization and atmospheric dynamics over 2015–2023 using CAMS data (3.1; Fig. 1 Block 1), including the evaluation of spatial patterns through cluster analysis compared with SAFRAN (3.1.1) and the role of atmospheric pressure as a key indicator of regional dynamics (3.1.2); and (ii) investigate CAMS as an integrated tool for meteorological and air quality diagnostics (3.2; Fig. 1 Block 2), focusing on the daily in situ PM₂.₅ response to regional-scale synoptic regimes (3.2.1) and the seasonal and interannual variability of PM₂.₅ under these regimes, with a comparison between two urban sites (3.2.2). 2. Material and methods 2.1. Data 2.1.1. Study area This study is conducted in Eastern France (Western Europe), a region characterized by temperate oceanic climate (Köppen's Cfb) (Köppen, 1936 ), defined by mild, wet winters and cool summers, influenced by the prevailing winds. The analysis was conducted at the regional scale within an area characterized by moderate relief (between 500 and 800 m) opening onto a basin where air can become trapped. The region experiences a temperate oceanic climate. Two study sites were selected due to their pronounced contrasts in local environmental conditions. Dijon, located on the plain in an urban and agricultural setting, and Montbéliard, on the edge of the Jura mountains, in a more isolated and forested environment. This difference allows us to assess the influence of the geographical context on the weather conditions observed and on PM pollution. 2.1.2. CAMS: atmospheric reanalysis (2015–2023) The meteorological data used in this study are the reanalyses provided by the Copernicus Atmospheric Monitoring Service (CAMS), a product developed by the European Centre for Medium-Range Weather Forecasts (ECMWF). CAMS provide atmospheric analyses and reanalyses by combining numerical models with satellite and in situ observations via a data assimilation system (Inness et al., 2019 ). In this work, we use the CAMS EAC4 reanalysis dataset, which covers the period from 1 January 2015 to 31 December 2022 with a horizontal spatial resolution of 0.75° × 0.75° and a 3 hours temporal frequency. The meteorological parameters selected – air temperature at 2 meters (t2m; in K), atmospheric pressure at sea level (msl; in hPa), zonal and meridional wind at 10 meters (u10 & v10 in m/s) and specific humidity (q; in kg/kg) – were chosen for their established roles in controlling particulate matter (PM) dynamics through processes of dispersion, accumulation and transformation. For example, Megaritis et al. ( 2014 ) showed that temperature and humidity affect aerosol partitioning and secondary formation, while wind speed and pressure gradients modulate dispersion and accumulation of PM₂.₅. A comparison is conducted between the weather classification derived from SAFRAN data and that obtained from CAMS data, using the specific variables listed for each dataset in Table 1 , to assess the consistency and complementarity of both approaches. To facilitate comparison between datasets, a summary table was constructed to cross-reference the key meteorological variables available in CAMS and SAFRAN (Table 1 ). Table 1 Weather parameters available and applied in SAFRAN (Marion et al., 2025 ) and CAMS and characteristics (spatial resolution, time step, study period) Average temperature at 2 metre (°C; t2m ) Wind (m/s; u & v ) Relative humidity (%; HR ) Atmospheric pressure (hPa; msl ) Rainfall (mm, RR ) Spatial resolution Time step Temporal period SAFRAN Available Available Available Not available Available VHR (8km) 1-day 2015–2023 CAMS Available Available Available Available Not available Coarse (80km) 3-hourly The data were processed and aggregated to a daily time step after extraction over a target geographical area encompassing a cross-border domain between France and Germany (63°N, 30°S; -20°W, 15°E) (Fig. 2 ). The relative humidity (RH, expressed in %) was calculated from the specific surface humidity obtained from CAMS (selected pressure level: 1000 hPa, close to the average pressure at sea level (1015 hPa) in a region dominated by plains or plateau) and the air temperature at 2 meters at the daily time step for each grid point, according to the formula based on the saturation vapor pressure of Tetens (Eq. 1): (1) $$\:RH\text{=}\frac{q\cdot\:P}{0.622\cdot\:es\left(T\right)}$$ where e s ( T ) is the saturation vapor pressure, calculated according to Clausius-Clapeyron’s law (Eq. 2): (2) $$\:es\left(T\right)\text{=}6.112\times\:exp\left(\frac{17.67\cdot\:T\text{-}273.15}{T\text{-}29.65}\right)$$ with q : specific humidity (kg/kg), P : air pressure (hPa), es​(T) : saturation vapor pressure (hPa), T : temperature in Kelvin (K). 2.1.2. PM in situ measurements The PM₁₀ and PM₂.₅ concentrations analyzed in this study were provided by the French AASQA network (Association Agréée de Surveillance de la Qualité de l’Air), specifically ATMO Bourgogne Franche Comté, for two urban background stations located in Dijon and Montbéliard, eastern France (black circles, Fig. 2 ). Background stations are intended to monitor the air quality representative of what most people are exposed to within urban areas (LCSQA, 2017). Hourly measurements were aggregated to a daily time step to match the temporal resolution of the CAMS dataset, covering the period from 1 January 2015 to 31 December 2022. Measurements were performed using Beta Attenuation Monitoring (BAM), currently recognized as one of the most reliable and accurate techniques for surface-level particulate matter monitoring (Shukla & Aggarwal, 2022 ). The BAM system samples particles (PM₁₀ and PM₂.₅) on a filter tape, which is subsequently exposed to a beta radiation source to determine particle mass at an hourly frequency. As particles accumulate on the filter, the attenuation of beta rays is measured, providing mass concentration data. According to Met One Instruments, ( 2024 ), the measurement uncertainty for BAM instruments is generally ± 5 µg/m³. This methodology ensures consistent, high-quality reference data suitable for comparison with model-based reanalysis products such as CAMS. 2.2. Analysis and classification methods based on CAMS 2.2.1. From Regional Structure to Daily Typology: A Multi-scale Clustering Approach Using CAMS Meteorological Data This section describes two analysis phases (Fig. 1 , Block 1). The first step consists of a spatial classification of weather parameters (Table 1 ) to evaluate the capacity of CAMS to reproduce regional-scale structures previously identified using the high-resolution SAFRAN reanalysis (Marion et al., 2025 ) (Fig. 1 , Block 1.a). This spatial clustering was applied over the particulate season, i.e. the period most favourable to PM 2.5 accumulation in eastern France. The second step focuses on a daily synoptic classification derived from CAMS surface pressure fields, aiming to identify the dominant large-scale circulation regimes influencing Western Europe and, by extension, the study area in eastern France (Fig. 1 , Block 1.b). Spatial analysis was conducted through a k-means clustering applied to three meteorological variables common to both SAFRAN and CAMS datasets (see Table 1 ), in order to test the ability of CAMS to reproduce regional-scale structures over the study period. The optimal number of clusters was determined using the Elbow method (Syakur et al., 2018 ), which minimizes within-cluster variance based on Euclidean distances. Circulation regimes were then derived from daily mean sea-level pressure fields, following approaches established in the literature (Davis et al., 1997 ). Each day between 2015 and 2023 was classified into one of three circulation types: anticyclonic, intermediate, or cyclonic. This procedure provides a systematic assignment of daily regimes, allowing for the analysis of their temporal distribution and their influence on regional meteorological conditions. By providing a synthetic representation of synoptic-scale dynamics, this approach facilitates the exploration of links between large-scale circulation patterns and surface-level air quality. Following the identification of dominant circulation regimes, the framework is further extended by integrating in situ PM₂.₅ measurements, allowing a direct assessment of the influence of synoptic conditions on particulate pollution dynamics. 2.2.2. Using circulation regimes to assess the local PM measurements Daily average concentrations of PM₂.₅ were extracted from the urban background stations of Dijon and Montbéliard, covering the period from 1 January 2015 to 31 December 2022. Each day was assigned to one of the previously defined circulation regimes (anticyclonic, cyclonic, or intermediate), enabling a systematic comparison between pollution levels and prevailing synoptic patterns at a daily time-scale. The objective is to assess the seasonal and interannual variability of PM₂.₅ concentrations under distinct circulation regimes. Two complementary approaches were adopted: (1) a focus on the winter period (December–January–February, DJF), which is known to favor pollution episodes in the region studied in eastern France; (2) An interannual perspective is also adopted to evaluate how frequently each circulation regime is associated with elevated or reduced pollution levels across different years. To support this analysis, the corresponding figures display, for each circulation regime, the distribution of daily PM₂.₅ concentrations during the DJF period over the eight-year study window. This provides a consistent framework for exploring both temporal variability and spatial contrasts in pollution response between two urban sites. To explore this topic further, we attempted to detect the number of atmospheric blockages, defined as “pollution episodes” where atmospheric pressure is equal to or higher than 1020 hPa over a period of more than five days (Ferreira et al., 2024). 3. Results 3.1. Climatological characterization and atmospheric regional dynamics based on CAMS (2015 – 2023) The aim of this section is first to evaluate the performance of CAMS in reproducing the regional-scale structures produced by the high-resolution SAFRAN reanalysis and the seasonal variability of meteorological conditions. Subsequently, the dynamics of local influence on a fine scale can be approached. Two levels of analysis are proposed: (i) the use of meteorological parameters to test spatialization based on CAMS and (ii) the use of pressure levels and their advantage in the classification of circulation regimes. 3.1.1. Evaluating Spatial Patterns in SAFRAN and CAMS Datasets through Cluster Analysis In Figure 2, the spatial patterns of CAMS and SAFRAN are in agreement. The lower resolution results in a substantially smaller number of grid points across the study domain (18 for CAMS versus 751 for SAFRAN). As summarized in Table 2, the clusters obtained from CAMS exhibit internal characteristics (relative humidity, temperature, and wind speed) broadly consistent with those identified using SAFRAN. Differences between the two datasets are systematic: CAMS clusters are associated with slightly higher mean temperatures (+0.5 to +0.9 °C) and wind speeds (+0.3 to +0.5 m s⁻¹), while relative humidity differences generally remain within ±3 %. Table 2 Average values of weather parameters used in SAFRAN and CAMS clustering (in bold) where RH (relative humidity, in %), t2m (temperature at 2 meters, in °C), VT (wind speed, in m/s). Liquid precipitation (RR) is not available in CAMS SAFRAN CAMS Number of grid point HR t2m VT RR Number of grid point HR t2m VT Cluster 1 321 84.6 6.01 3.08 2.34 8 82.2 6.94 3.58 Cluster 2 143 82.8 4.34 2.19 3.65 3 82.8 4.89 2.42 Cluster 3 287 83.3 6.57 2.74 2.30 6 80.1 6.94 3.58 Cluster 4 1 91.3 1.25 1.69 Four clusters are identified : Cluster 1 (blue, fig. 2) covers lowland areas (0–400 m) with open topography, warmer conditions (6.94°C), and higher wind speeds (3.58m/s). Cluster 2 (red, fig. 2) corresponds to a small number of high-altitude mountainous pixels characterized by cold (4.89°C), humid (82.8%), and stable winter conditions. Cluster 3 (purple, fig. 2) is associated with enclosed valleys, including parts of the Doubs basin, less humid (80.1%), well ventilated (3.58m/s). Cluster 4 (green, fig. 2) corresponds to a high-altitude zone at the French–Swiss border. Its characteristics largely reflect the spatial resolution of the CAMS grid; this cluster is therefore excluded from subsequent analyses. Cluster 1, corresponding to lowland areas including Dijon, shows consistently warmer and windier conditions in CAMS than in SAFRAN (Tmean: 6.01 °C vs. 6.94 °C; wind speed: 3.08 m s⁻¹ vs. 3.58 m s⁻¹), while Cluster 3, associated with valley environments, exhibits intermediate values for all three variables. Cluster 2 groups high-altitude areas such as parts of the Jura and the Alps, characterized by colder and more humid conditions in both datasets (Tmean: 4.34 °C [SAFRAN] vs. 1.25 °C [CAMS]; RH: 82.8 % vs. 91.3 %). Cluster 4, represented by a single CAMS pixel at the French–Swiss border, displays very high relative humidity (91.3 %) and low mean temperature (1.25 °C), corresponding to high-altitude or fog-prone winter regimes. For these reasons, it was excluded from further analysis to prevent extreme or highly localized conditions from biasing the assessment of the main urban and valley environments. The two urban sites are distinctly represented: Dijon consistently falls within Cluster 1 in both datasets, while Montbéliard is assigned to Cluster 3 in SAFRAN and to Cluster 2 in CAMS. The distribution of pixels across the three selected clusters highlights differences in spatial granularity between the datasets: in CAMS, Cluster 1 covers 8 pixels (44 %), Cluster 2 3 pixels (17 %), Cluster 3 6 pixels (33 %), and Cluster 4 a single pixel (6 %). In comparison, SAFRAN assigns 321 pixels (43 %) to Cluster 1, 143 pixels (19 %) to Cluster 2, and 287 pixels (38 %) to Cluster 3. A comparative analysis of the spatial clustering results obtained from CAMS meteorological data and from the SAFRAN-based classification of Marion et al. (2025) indicates that CAMS produces regional structures comparable to those derived from SAFRAN (fig. 2), despite its coarser spatial resolution (~80 km compared to 8 km). 3.1.2. Atmospheric pressure: a key indicator of regional dynamics To complement the spatial clustering of weather parameters, a temporal dimension was added by classifying the daily evolution of sea-level atmospheric pressure over the period 2015–2023 using CAMS data. Based on daily mean pressure in eastern France (including the 2 urban sites Dijon and Montbéliard), three circulation regimes were identified (fig. 3): Low-pressure regime (pressure 1020 hPa, orange). The day-by-day classification was performed for each day over the entire period 2015–2023, but the analysis presented here focuses specifically on the year 2020. Figure 3.a shows the daily evolution of mean regional pressure in 2020, with the black curve representing the pressure and the coloured bands indicating the assigned regime for each day. In 2020, low-pressure regimes accounted for 16.2 % of days, intermediate regimes for 55.9 %, and high-pressure regimes for 27.9 %. Figure 3.b presents the distribution of meteorological variables (relative humidity, mean temperature, wind speed) during the particulate season in 2020 for each regime. Anticyclonic conditions are associated with lower wind speeds (~2.5 m s⁻¹), whereas both low- and intermediate-pressure regimes display higher values. Figure 3.c details these variables separately for Dijon and Montbéliard, showing spatial differences between the two urban sites. These contrasts illustrate the local heterogeneity of meteorological conditions within the study region. Thus, while CAMS reproduces well the major regional meteorological dynamics despite coarser spatial resolution, its real added value lies in the availability of daily sea-level pressure (not available in SAFRAN). This parameter enables a quantitative and robust day-to-day characterization of atmospheric circulation regimes, enhancing the ability to link these regimes to particulate pollution dynamics in the region. The following analysis therefore focuses on the impact of these regimes on in situ particulate matter pollution measurements. 3.2. CAMS as an integrated tool for meteorological and air quality diagnostics This section evaluates CAMS as an integrated tool for characterizing regional atmospheric dynamics via sea-level pressure and assessing urban-scale particulate pollution. The regime-based analysis confirms that CAMS reliably captures dominant circulation structures and enables the definition of meaningful meteorological regimes throughout the particulate season, particularly during the peak winter months (DJF: December – January – February). We further explore how these synoptic regimes influence in situ PM₂.₅ concentrations and evaluate CAMS’s ability to capture spatial contrasts between two nearby urban stations with distinct local environments, highlighting its potential to link synoptic-scale dynamics with localized pollution patterns. The analysis of average PM₂.₅ concentrations measured at the Montbéliard (dotted line in Fig. 4) and Dijon (solid line in Fig. 4) stations covers the period 2015–2022 and presents the mean over eight years. A marked seasonality of PM₂.₅ is observed, with the highest concentrations occurring during winter (DJF), coinciding with the heart of the particulate season in eastern France (Fig. 4). Anticyclonic conditions (pressure > 1020 hPa) are associated with the highest average concentrations: Montbéliard 19.0 ± 5.11 µg/m³ (min: 12.6; max: 29.1) and Dijon 14.7 ± 4.07 µg/m³ (min: 10.4; max: 23.3) (Table 2). Intermediate conditions yield moderate values (Montbéliard 16.5 ± 4.92 µg/m³; Dijon 12.8 ± 4.66 µg/m³), while low-pressure regimes (< 1010 hPa) show the lowest averages (Montbéliard 10.3 ± 2.16 µg/m³; Dijon 7.84 ± 2.96 µg/m³). Table 3 Statistics of PM 2.5 concentrations measured at the stations (Dijon and Montbéliard) according to Circulation regimes for all winters (DJF) over the period 2015–2023 Station Anticyclonic (A) Intermediate (I) Low pressure (L) Dijon Mean 14.7 12.8 7.84 StDev 4.07 4.66 2.96 Max 23.3 20.6 13 Min 10.4 7.29 3.48 Montbéliard Mean 19 16.5 10.3 StDev 5.11 4.92 2.16 Max 29.1 24.1 14.6 Min 12.6 8.05 7.40 Across all circulation regimes, Montbéliard records higher mean concentrations than Dijon. Seasonal contrasts are strongest in winter, when anticyclonic conditions are most frequent, with an average difference of +4.3 µg/m³ between the two stations. The concentration range (min–max) is also larger in Montbéliard than in Dijon, indicating greater variability at this site, particularly under anticyclonic conditions. To investigate this study further, it is necessary to consider these observations in a broader temporal context in order to assess the influence of synoptic patterns on PM 2.5 seasonal and interannual variability. 3.2.2. Seasonal and interannual variability of PM₂.₅ under synoptic regimes: comparing two urban sites The relationship between the frequency of synoptic weather systems and average PM₂.₅ concentrations is shown in Figure 5 for Montbéliard and Dijon. In Montbéliard, anticyclonic conditions show a strong positive correlation with PM₂.₅ concentrations (r = 0.90; p = 0.002) Intermediate conditions (r = –0.26; p = 0.50) and low-pressure systems (r = 0.50; p = 0.216) exhibit no significant correlation. In Dijon, intermediate weather systems show a negative correlation with PM₂.₅ (r = –0.75; p = 0.019), while low-pressure systems show a positive correlation (r = 0.78; p = 0.023). The correlation with high-pressure systems is moderate and borderline significant (r = 0.66; p = 0.051). Overall, the analysis indicates that the relationship between synoptic regimes and PM₂.₅ concentrations is more pronounced at Dijon than at Montbéliard. At both sites, anticyclonic (high-pressure) conditions consistently show the strongest positive correlations with PM₂.₅, highlighting the key role of stagnant, high-pressure weather in promoting pollution accumulation. The weaker or non-significant correlations under intermediate or low-pressure systems may reflect the influence of local topography, valley effects, and differing emission sources that modulate the response of particulate concentrations to synoptic conditions. A strong working hypothesis is that atmospheric blocking events, characterized by persistent high-pressure systems, are likely to drive prolonged episodes of elevated PM₂.₅, although the magnitude and spatial extent of the effect may vary depending on urban morphology, ventilation patterns, and seasonal emission profiles. Quantifying this influence remains a priority for improving the predictive understanding of pollution episodes in contrasting urban environments. The scatter points in Figure 6 indicate several atypical winters that deviate from general trends, highlighting a case-study perspective on the interplay between synoptic regimes and local PM₂.₅ concentrations. Specific years identified include DJF 2015–2016, DJF 2016–2017, DJF 2018–2019, and DJF 2019–2020. During DJF 2016–2017 and DJF 2015–2016, PM₂.₅ concentrations reached up to ~28–30 µg/m³, coinciding with high anticyclonic frequency, while DJF 2019–2020 exhibited lower PM₂.₅ levels alongside a lower frequency of anticyclones. For atmospheric blockages (defined as >1025 hPa for more than five consecutive days), anticyclonic conditions were associated with more than 50 blockage days, average seasonal wind speeds of 1.84 m/s, and average temperatures of –0.50 °C. In Dijon, DJF 2016–2017 was characterized by lower temperatures (~1 °C) and PM₂.₅ concentrations above the general trend. Under low-pressure conditions, DJF 2018–2019 and DJF 2015–2016 at PEJ exhibited higher PM₂.₅ levels, with average wind speeds of ~5.08 m/s and blockages persisting for 50 days. At LEV, PM₂.₅ remained elevated during DJF 2016–2017 despite average low-pressure frequency. Overall, these observations illustrate that while high-pressure anticyclonic blockages generally drive elevated PM₂.₅ concentrations, local factors such as wind speed, temperature, and topography can modulate deviations from the general seasonal and interannual trends. 4. Discussion 4.1. Spatialization of Regional Weather Structures Our comparison of CAMS and SAFRAN classifications shows that, despite its coarser horizontal resolution (~ 80 km versus ~ 8 km for SAFRAN), CAMS reproduces the main regional meteorological structures over eastern France (Bourgogne Franche Comté). High-altitude areas (Cluster 2) systematically emerge as colder and more humid, while lowlands (Cluster 1) remain warmer and windier, and valleys (Cluster 3) display intermediate conditions. These results are consistent with previous validations of reanalysis products against fine-resolution observations. For instance, ERA5 has been shown to capture European wind speed climatologies with high accuracy compared to dense observational networks (Molina et al., 2021 ). Similarly, CAMS global reanalysis has demonstrated skill in reproducing surface PM₂.₅ concentrations in complex environments such as the São Paulo metropolitan area, with correlations ranging from 0.75 to 0.89 (Damascena et al., 2021 ). The smoothing of microclimatic features, such as the Morvan hills or Jura subregions, reflects the intrinsic limitations of coarse-resolution reanalyses, as already noted in comparisons between global reanalyses and high-resolution regional models (e.g., ERA5 vs. SAFRAN; (Quintana-Seguí et al., 2008 )) This underlines that downscaling or high-resolution models remain essential for intra-urban or local-scale analyses. Nevertheless, CAMS captures the dominant spatial contrasts robustly, providing a reliable basis for regional-scale meteorological diagnostics. 4.2. Sea-Level pressure classification The use of sea-level pressure at a 3-hourly temporal resolution provides a quantitative and reproducible framework for defining circulation regimes, reducing the subjectivity inherent in traditional synoptic classifications such as the Groβwetterlagen (GWL) approach (Philipp et al., 2007 ; Trigo & DaCamara, 2000 ). Unlike conventional indices that rely on daily averages or subjective pattern recognition, the high temporal resolution of CAMS enables the rapid evolution of synoptic structures to be tracked, offering a finer representation of intraday variability. This is particularly relevant for diagnosing pollution episodes, which often respond to transient circulation features that may be overlooked in lower-resolution datasets. A key result of our analysis is the identification of an intermediate pressure regime, bridging the conventional binary distinction between anticyclonic (> 1020 hPa) and low-pressure conditions. Transitional regimes were especially frequent in spring and autumn, when moderate stability dominates. The relevance of such intermediate states has been emphasized in recent research: Cattiaux et al. ( 2016 ) showed that variations in the sinuosity of midlatitude atmospheric flows modulate the persistence of surface weather regimes, directly impacting pollutant accumulation. Similarly, Huang et al. ( 2019 ) demonstrated that intermediate circulation states and associated wind variability strongly control pollutant dispersion in urban street canyons, highlighting the need to move beyond binary classifications. In the 2020 dataset, intermediate regimes predominated (55.9% of days), with anticyclonic conditions representing 27.9% and low-pressure systems 16.2%. This confirms that including transitional regimes provides a more complete and realistic depiction of regional atmospheric dynamics. The contrast observed between Dijon and Montbéliard illustrates how local heterogeneities (topography, valley orientation, land use) modulate the regional circulation imprint. For instance, Montbéliard exhibited higher humidity and more frequent stagnation episodes, favoring PM₂.₅ accumulation. Comparable results were reported by Zhu et al. ( 2023 ) in the Eastern Monsoon Region of China, where interactions between synoptic weather types and complex terrain amplified local contrasts in PM₂.₅ levels. Overall, these results demonstrate that CAMS, by providing high-frequency sea-level pressure data, enables a robust and reproducible classification of circulation regimes. This approach improves our understanding of the meteorological drivers of particulate pollution and holds potential for operational applications in air quality management. 4.3. PM 2.5 response to synoptic regimes Consistent with previous research, our results show that PM₂.₅ concentrations are systematically highest under anticyclonic regimes than in the 2 other regimes, confirming the role of atmospheric stability and suppressed vertical mixing in promoting particle accumulation. These conditions are particularly critical in winter, when thermal inversions, low wind speeds, and reduced turbulence favor pollutant persistence in the lower atmosphere. For example, Largeron & Staquet ( 2016 ) demonstrated that persistent thermal inversions in Alpine valleys (Grenoble area) strongly enhance PM₁₀ accumulation during winter pollution episodes. Similar findings across Europe highlight how stable high-pressure systems amplify stagnation conditions and pollutant build-up (Pearce et al., 2011 ; Vautard et al., 2007 ). In contrast, low-pressure regimes are consistently associated with lower PM₂.₅ levels, reflecting enhanced ventilation, stronger turbulence, and more efficient dispersion. This dichotomy between high- and low-pressure systems reinforces the concept that synoptic circulation patterns are a primary driver of seasonal and episodic variations in fine particulate matter, in line with studies linking large-scale meteorology to urban air quality (Leung et al., 2018 ; Pope et al., 2016 ). A particularly noteworthy result of our analysis is the role of the intermediate pressure regime. While its meteorological structure suggests moderate stability, it exhibits variable impacts on PM₂.₅ concentrations. This highlights the potential of such transitional states to capture nuanced conditions – especially in spring and autumn – that are often overlooked by binary classifications. It also raises the prospect that this intermediate regime may provide a valuable diagnostic tool for identifying pollution-prone conditions beyond the simplistic high/low-pressure distinction. The analysis of in situ PM₂.₅ data clearly reflects the CAMS-derived circulation regimes. Prolonged anticyclonic events (> 1020 hPa sustained for ≥ 5 days) correspond to the highest concentrations observed, demonstrating that the persistence of stable high-pressure systems is a key determinant of extreme pollution episodes. Conversely, depressionary conditions (< 1010 hPa) systematically enhance particle dispersion, underlining the importance of regime type, duration, and seasonality in shaping air quality. Together, these results demonstrate that coupling synoptic-scale classifications from CAMS with surface observations provides a robust framework for diagnosing and predicting air pollution episodes. Beyond its relevance for northeast France, this approach is potentially transferable to other regions, particularly where observational networks are sparse and reliance on reanalyses is critical for air quality management. 4.4. Local contrast at urban scale Despite the robust relationship between synoptic-scale circulation and PM₂.₅ described above, the observed interannual variability demonstrates that meteorology alone cannot fully explain fluctuations in particulate concentrations. As widely documented, the proximity of local emission sources – including traffic, residential heating, and industry – remains decisive in shaping both baseline levels and pollution peaks. For example, study in Eastern Europe by Juda-Rezler et al. ( 2020 ) identified residential combustion (46%) and traffic emissions (~ 31%) as dominant contributors to PM₂.₅, with significant seasonal variability – highlighting the interplay between local sources and meteorological drivers. Similarly, residential heating is a dominant contributor in winter, accounting for up to 80–90% of PM in some Slovakian cities, particularly during cold spells and stable conditions (Jandacka et al., 2024 ). In heavily industrialized regions such as the Beijing–Tianjin–Hebei (BTH) megacity cluster, industrial sources are responsible for nearly one-third of PM₂.₅, with peak contributions in autumn and winter when stagnant synoptic patterns favor accumulation (Zeng et al., 2024 ). These examples confirm that traffic, heating, and industrial emissions are critical determinants of urban PM₂.₅, with their influence strongly modulated by seasonal meteorology. This limitation reflects the coarse spatial resolution of CAMS (~ 80 km) and the absence of detailed surface data assimilation, which constrain its ability to capture intra-urban contrasts. Previous evaluations have shown similar limitations for global reanalyses when compared with high-resolution regional models (Quintana-Seguí et al., 2008 ). The two urban sites analyzed, Montbéliard and Dijon, exemplify this scale mismatch. Montbéliard consistently exhibits higher mean concentrations and greater variability across all synoptic regimes, especially during winter anticyclonic episodes. By contrast, Dijon, located in a more open topographic setting, shows lower and more stable levels. These differences are consistent with topographic influences: valley confinement and reduced ventilation in Montbéliard favor pollutant accumulation, while Dijon benefits from enhanced mixing. Similar findings have been reported in Alpine and Central European valleys, where terrain-induced stagnation amplifies pollution episodes under synoptic blocking. For example, Rodrı́guez et al. ( 2001 ) illustrated this effect in Southern and Eastern Spain, showing how topography and the transport of Saharan dust interact with stagnant atmospheric conditions to increase PM 10 concentrations. Such site-specific variability underscores that meteorology alone cannot account for interannual fluctuations in PM₂.₅. Instead, local emissions, topography, and regional circulation interact to determine pollution levels. The analysis of atypical winters – characterized by persistent blocking anticyclones lasting ≥ 5 days – further illustrates this interplay: extreme pollution events occur when synoptic-scale stagnation coincides with local conditions favorable to accumulation. Overall, these findings highlight the need to integrate high-resolution emission inventories and local-scale modeling (e.g., SIRANE, ADMS-Urban) with CAMS-derived synoptic regimes. Such a multi-scale approach would improve the understanding of wintertime pollution episodes and provide more operationally relevant tools for urban air quality management. 5. Conclusion Our results show that CAMS provides a robust representation of regional synoptic patterns, reproducing the dominant spatial structures observed in higher-resolution SAFRAN datasets. Despite its coarse resolution (~ 80 km), CAMS captures the key contrasts between highlands, lowlands, and valleys in Bourgogne Franche Comté, confirming its utility for regional meteorological diagnostics. The availability of high-temporal-resolution sea-level pressure data allows a quantitative and reproducible day-by-day classification of circulation regimes. Anticyclonic conditions are associated with elevated PM₂.₅ through enhanced atmospheric stability and reduced ventilation, whereas low-pressure systems favor pollutant dispersion—patterns fully consistent with previous studies. Importantly, our analysis highlights an intermediate circulation regime, which exhibits distinct PM₂.₅ behavior and demonstrates that daily synoptic classification can provide insights beyond the conventional high- and low-pressure extremes. The study also shows that meteorology alone cannot explain all interannual variability of PM₂.₅, emphasizing the critical role of local emission sources and topographic effects. Coarse-resolution reanalyses like CAMS are therefore insufficient for street-level assessments, where sub-kilometer heterogeneities must be resolved with high-resolution urban models (e.g., SIRANE). Taken together, these results demonstrate that CAMS offers a coherent synoptic-scale framework for analyzing, forecasting, and managing wintertime particulate pollution episodes. Its ability to link circulation regimes to PM₂.₅ variability provides operationally relevant information for air quality management, while the methodology can be transposed and scaled to other regions with limited observational networks. In combination with local emission inventories and fine-scale dispersion models, CAMS thus constitutes a powerful tool for integrated environmental and urban air quality studies. Declarations The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. The authors have no relevant financial or non-financial interests to disclose. Author Contribution S.M wrote the main manuscript text. N.M and P.R have reviewed the document, making amendments and providing recommendations. All authors have reviewed the manuscript and approved its final version. References Artı́ñano, B., Salvador, P., Alonso, D. G., Querol, X., & Alastuey, A. (2003). 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13:48:24","extension":"xml","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":134880,"visible":true,"origin":"","legend":"","description":"","filename":"16333221801545b38a87d0b4193a2aed1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7564554/v1/c5114e64d2bdb1bcb514cd3b.xml"},{"id":93234577,"identity":"8b2af4ee-6ece-47f3-9c0e-de45e4ea1631","added_by":"auto","created_at":"2025-10-10 13:48:24","extension":"html","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":139957,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7564554/v1/4e3ea8e015eb616e688c2864.html"},{"id":93234556,"identity":"b60cbab8-1994-45e4-b93e-9f7f6c094474","added_by":"auto","created_at":"2025-10-10 13:48:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":65949,"visible":true,"origin":"","legend":"\u003cp\u003eMethodological diagram of the two main phases of the analysis (Block 1. Climate; Block2. Air quality). The diagram shows objectives (light blue), input variables (orange), methods (light grey, black border), main results (red), and elements used for comparison (dark blue, from Marion et al., 2025)\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7564554/v1/f22d2cd4ba4c41f72bb77045.png"},{"id":93234557,"identity":"17468987-e4d5-49f1-a0f9-170198295cc4","added_by":"auto","created_at":"2025-10-10 13:48:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":115557,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSpatial clustering of weather types based on SAFRAN data (from Marion et al., 2025) (left) and CAMS data (right). Cluster 1 is blue, cluster 2 is red, and cluster 3 is purple. For CAMS, cluster 4 is green. The two sites of Dijon and Montbéliard are symbolized by the two black circles\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7564554/v1/a162f75e54f39350df3e25cb.png"},{"id":93234559,"identity":"af1067f2-88d5-4e16-bfc5-ffc555420e35","added_by":"auto","created_at":"2025-10-10 13:48:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":224246,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003c/em\u003e\u003cem\u003eCirculation regimes day by day in eastern France (2020). \u003c/em\u003e\u003cem\u003e\u003cstrong\u003e3.b\u003c/strong\u003e\u003c/em\u003e\u003cem\u003eBoxplot of meteorological parameters (relative humidity, average temperature and wind speed) for each circulation regime during the particulate season.\u003c/em\u003e\u003cem\u003e\u003cstrong\u003e 3.c \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eBoxplot of meteorological variables during the particulate season in Dijon and Montbéliard. Anticyclonic regimes are represented in orange, intermediate regimes in light blue and low-pressure systems in dark blue\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7564554/v1/d237f497cb16ef2d2f59f001.png"},{"id":93234565,"identity":"074af36c-d8ad-4de3-8200-9d49a2581bc1","added_by":"auto","created_at":"2025-10-10 13:48:24","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":167443,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Average daily atmospheric pressure over the period 2015–2023. Anticyclonic patterns are represented in orange, intermediate patterns in light blue and low-pressure patterns in dark blue. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003e4.b \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eAverage PM\u003c/em\u003e\u003csub\u003e\u003cem\u003e2.5 \u003c/em\u003e\u003c/sub\u003e\u003cem\u003econcentrations in Dijon (solid line) and Montbéliard (dashed line) over the period 2015–2023\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7564554/v1/2f95a2103d50d0330fcfcc35.png"},{"id":93234560,"identity":"c5b35b3e-9e52-43fd-8e63-b7377ccd6754","added_by":"auto","created_at":"2025-10-10 13:48:24","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":127592,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eAverage PM\u003c/em\u003e\u003csub\u003e\u003cem\u003e2.5 \u003c/em\u003e\u003c/sub\u003e\u003cem\u003econcentration levels according to the frequency of the three types of synoptic regimes - anticyclonic, intermediate and low pressure - for each winter (DJF) over the period 2015–2023. Dijon is symbolised by an empty black circle and Montbéliard by a solid navy blue triangle. The dotted lines represent the linear trend\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7564554/v1/b583fb7127d7c0e634bbaa50.png"},{"id":93234561,"identity":"6ead588c-5ff0-4322-afc8-42758ed065fc","added_by":"auto","created_at":"2025-10-10 13:48:24","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":49289,"visible":true,"origin":"","legend":"\u003cp\u003eAverage PM\u003csub\u003e2.5 \u003c/sub\u003econcentration levels according to the number of days of atmospheric blockages during anticyclonic episodes (\u0026gt; 1020 hPa) for each winter (DJF) over the period 2015–2023. PEJ is symbolised by an empty black circle and LEV by a solid navy blue triangle.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7564554/v1/825514b27afd9030ed10c70c.png"},{"id":102710415,"identity":"df56fe9d-d483-4ae9-9676-08aab5d047cf","added_by":"auto","created_at":"2026-02-15 15:10:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1597106,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7564554/v1/22bf426d-c4f3-493b-aef9-89376e7aa491.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"CAMS products for analyzing atmospheric dynamics to develop QA indicators at regional scale","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eParticulate matter (PM) is commonly classified by aerodynamic diameter into PM₁₀, PM₂.₅, and PM₁, with smaller particles penetrating deeper into the respiratory system. It therefore represents a major public health concern due to its association with respiratory and cardiovascular diseases (Brunekreef et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; J. Chen \u0026amp; Hoek, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Fiordelisi et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In Europe, air quality is monitored through regulatory networks providing surface concentrations, with thresholds defined by European legislation (PM₁₀: 50 \u0026micro;g/m\u0026sup3; for 24 h, 40 \u0026micro;g/m\u0026sup3; annual; PM₂.₅: 25 \u0026micro;g/m\u0026sup3; annual) and increasingly guided by stricter World Health Organization (WHO) recommendations (PM₁₀: 45 \u0026micro;g/m\u0026sup3; 24 h, 15 \u0026micro;g/m\u0026sup3; annual; PM₂.₅: 15 \u0026micro;g/m\u0026sup3; 24 h, 5 \u0026micro;g/m\u0026sup3; annual).\u003c/p\u003e\u003cp\u003eUrban PM concentrations result from a complex interplay of emission sources \u0026ndash; natural (e.g., desert dust, wildfires; (Artı́\u0026ntilde;ano et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2003\u003c/span\u003e)) and anthropogenic (e.g., residential heating, traffic; (Kassomenos et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e)) \u0026ndash; and meteorological conditions (wind, temperature inversions, humidity) that govern dispersion, accumulation, and wet deposition (Bodor et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Long-range transport can sustain elevated pollution over several days even in the absence of strong local emissions (Salvador et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Waked et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Topography amplifies these effects, particularly in valleys during winter anticyclonic conditions. Despite numerous studies linking meteorology and PM concentrations, few have addressed their combined spatiotemporal variability in urban contexts (Z. Chen et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). For instance, Yang et al., (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) explored relationships between PM₂.₅ and meteorological factors across 74 major Chinese cities over nearly two years, revealing that relative humidity correlates positively with PM₂.₅ in northern regions but negatively elsewhere, while wind speed uniformly shows a negative correlation except over Hainan; correlations with temperature and pressure also vary seasonally and regionally. Additionally, Huszar et al., (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) quantified how urbanization in central Europe modifies PM₂.₅ concentrations, disentangling contributions from urban canopy meteorological forcing and altered deposition and emission patterns, and found that urban-specific meteorological changes can slightly reduce PM₂.₅ while emissions dominate increases\u003c/p\u003e\u003cp\u003ePrevious work in the north-eastern French region of Bourgogne Franche Comt\u0026eacute; (Marion et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) highlighted the central role of atmospheric circulation in shaping PM patterns, using high-resolution SAFRAN data (8 km) combined with Gro\u0026szlig;wetterlagen (GWL) classifications to capture both spatial and temporal dynamics. Building on this, the present study evaluates the Copernicus Atmosphere Monitoring Service (CAMS) as a multi-scale tool for diagnosing PM₂.₅ pollution. CAMS provides a consistent and complete database that aims to establish a replicable method and approach. Despite its coarser resolution (~\u0026thinsp;80 km), CAMS provides consistent, high-frequency meteorological and aerosol data across Europe, making it suitable for synoptic-scale analyses.\u003c/p\u003e\u003cp\u003eThis study addresses two main research questions: to what extent can surface pressure fields and circulation regimes derived from CAMS reanalysis reproduce regional-scale atmospheric structures previously identified with higher-resolution datasets? Can CAMS-based synoptic regimes \u0026ndash; particularly anticyclonic blocking and intermediate situations \u0026ndash; explain the observed variability of daily and seasonal PM₂.₅ concentrations, and thus serve as indicators for anticipating winter pollution episodes? More generally, can CAMS-derived circulation regimes and the associated methodology be transferred to different geographical areas and larger spatial scales, enabling broader applications in air quality management?\u003c/p\u003e\u003cp\u003eThe primary objective is to evaluate whether CAMS can support the development of a daily-scale, meteorologically driven classification of atmospheric situations relevant to PM₂.₅ accumulation and dispersion. Specifically, we aim to: (i) assess the climatological characterization and atmospheric dynamics over 2015\u0026ndash;2023 using CAMS data (3.1; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e Block 1), including the evaluation of spatial patterns through cluster analysis compared with SAFRAN (3.1.1) and the role of atmospheric pressure as a key indicator of regional dynamics (3.1.2); and (ii) investigate CAMS as an integrated tool for meteorological and air quality diagnostics (3.2; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e Block 2), focusing on the daily in situ PM₂.₅ response to regional-scale synoptic regimes (3.2.1) and the seasonal and interannual variability of PM₂.₅ under these regimes, with a comparison between two urban sites (3.2.2).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"2. Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Data\u003c/h2\u003e\u003cdiv id=\"Sec4\" class=\"Section3\"\u003e\u003ch2\u003e2.1.1. Study area\u003c/h2\u003e\u003cp\u003eThis study is conducted in Eastern France (Western Europe), a region characterized by temperate oceanic climate (K\u0026ouml;ppen's Cfb) (K\u0026ouml;ppen, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1936\u003c/span\u003e), defined by mild, wet winters and cool summers, influenced by the prevailing winds. The analysis was conducted at the regional scale within an area characterized by moderate relief (between 500 and 800 m) opening onto a basin where air can become trapped. The region experiences a temperate oceanic climate. Two study sites were selected due to their pronounced contrasts in local environmental conditions. Dijon, located on the plain in an urban and agricultural setting, and Montb\u0026eacute;liard, on the edge of the Jura mountains, in a more isolated and forested environment. This difference allows us to assess the influence of the geographical context on the weather conditions observed and on PM pollution.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003e2.1.2. CAMS: atmospheric reanalysis (2015\u0026ndash;2023)\u003c/h2\u003e\u003cp\u003eThe meteorological data used in this study are the reanalyses provided by the Copernicus Atmospheric Monitoring Service (CAMS), a product developed by the European Centre for Medium-Range Weather Forecasts (ECMWF). CAMS provide atmospheric analyses and reanalyses by combining numerical models with satellite and in situ observations via a data assimilation system (Inness et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In this work, we use the CAMS EAC4 reanalysis dataset, which covers the period from 1 January 2015 to 31 December 2022 with a horizontal spatial resolution of 0.75\u0026deg; \u0026times; 0.75\u0026deg; and a 3 hours temporal frequency. The meteorological parameters selected \u0026ndash; air temperature at 2 meters (t2m; in K), atmospheric pressure at sea level (msl; in hPa), zonal and meridional wind at 10 meters (u10 \u0026amp; v10 in m/s) and specific humidity (q; in kg/kg) \u0026ndash; were chosen for their established roles in controlling particulate matter (PM) dynamics through processes of dispersion, accumulation and transformation. For example, Megaritis et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) showed that temperature and humidity affect aerosol partitioning and secondary formation, while wind speed and pressure gradients modulate dispersion and accumulation of PM₂.₅. A comparison is conducted between the weather classification derived from SAFRAN data and that obtained from CAMS data, using the specific variables listed for each dataset in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, to assess the consistency and complementarity of both approaches. To facilitate comparison between datasets, a summary table was constructed to cross-reference the key meteorological variables available in CAMS and SAFRAN (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eWeather parameters available and applied in SAFRAN (Marion et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and CAMS and characteristics (spatial resolution, time step, study period)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAverage temperature at 2 metre (\u0026deg;C; \u003cem\u003et2m\u003c/em\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWind (m/s; \u003cem\u003eu \u0026amp; v\u003c/em\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRelative humidity (%; \u003cem\u003eHR\u003c/em\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAtmospheric pressure (hPa; \u003cem\u003emsl\u003c/em\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRainfall (mm, \u003cem\u003eRR\u003c/em\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSpatial resolution\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eTime step\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eTemporal period\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSAFRAN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAvailable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAvailable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAvailable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNot available\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAvailable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eVHR (8km)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1-day\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e2015\u0026ndash;2023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCAMS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAvailable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAvailable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAvailable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAvailable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNot available\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCoarse (80km)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3-hourly\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe data were processed and aggregated to a daily time step after extraction over a target geographical area encompassing a cross-border domain between France and Germany (63\u0026deg;N, 30\u0026deg;S; -20\u0026deg;W, 15\u0026deg;E) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe relative humidity (RH, expressed in %) was calculated from the specific surface humidity obtained from CAMS (selected pressure level: 1000 hPa, close to the average pressure at sea level (1015 hPa) in a region dominated by plains or plateau) and the air temperature at 2 meters at the daily time step for each grid point, according to the formula based on the saturation vapor pressure of Tetens (Eq.\u0026nbsp;1):\u003c/p\u003e\u003cp\u003e(1)\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:RH\\text{=}\\frac{q\\cdot\\:P}{0.622\\cdot\\:es\\left(T\\right)}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e(\u003cem\u003eT\u003c/em\u003e) is the saturation vapor pressure, calculated according to Clausius-Clapeyron\u0026rsquo;s law (Eq.\u0026nbsp;2):\u003c/p\u003e\u003cp\u003e(2)\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:es\\left(T\\right)\\text{=}6.112\\times\\:exp\\left(\\frac{17.67\\cdot\\:T\\text{-}273.15}{T\\text{-}29.65}\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewith \u003cem\u003eq\u003c/em\u003e : specific humidity (kg/kg), \u003cem\u003eP\u003c/em\u003e : air pressure (hPa), \u003cem\u003ees​(T)\u003c/em\u003e : saturation vapor pressure (hPa), \u003cem\u003eT\u003c/em\u003e : temperature in Kelvin (K).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.1.2. PM in situ measurements\u003c/h2\u003e\u003cp\u003eThe PM₁₀ and PM₂.₅ concentrations analyzed in this study were provided by the French AASQA network (Association Agr\u0026eacute;\u0026eacute;e de Surveillance de la Qualit\u0026eacute; de l\u0026rsquo;Air), specifically ATMO Bourgogne Franche Comt\u0026eacute;, for two urban background stations located in Dijon and Montb\u0026eacute;liard, eastern France (black circles, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Background stations are intended to monitor the air quality representative of what most people are exposed to within urban areas (LCSQA, 2017). Hourly measurements were aggregated to a daily time step to match the temporal resolution of the CAMS dataset, covering the period from 1 January 2015 to 31 December 2022.\u003c/p\u003e\u003cp\u003eMeasurements were performed using Beta Attenuation Monitoring (BAM), currently recognized as one of the most reliable and accurate techniques for surface-level particulate matter monitoring (Shukla \u0026amp; Aggarwal, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The BAM system samples particles (PM₁₀ and PM₂.₅) on a filter tape, which is subsequently exposed to a beta radiation source to determine particle mass at an hourly frequency. As particles accumulate on the filter, the attenuation of beta rays is measured, providing mass concentration data. According to Met One Instruments, (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), the measurement uncertainty for BAM instruments is generally\u0026thinsp;\u0026plusmn;\u0026thinsp;5 \u0026micro;g/m\u0026sup3;. This methodology ensures consistent, high-quality reference data suitable for comparison with model-based reanalysis products such as CAMS.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Analysis and classification methods based on CAMS\u003c/h2\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e2.2.1. From Regional Structure to Daily Typology: A Multi-scale Clustering Approach Using CAMS Meteorological Data\u003c/h2\u003e\u003cp\u003eThis section describes two analysis phases (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Block 1). The first step consists of a spatial classification of weather parameters (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) to evaluate the capacity of CAMS to reproduce regional-scale structures previously identified using the high-resolution SAFRAN reanalysis (Marion et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Block 1.a). This spatial clustering was applied over the particulate season, i.e. the period most favourable to PM\u003csub\u003e2.5\u003c/sub\u003e accumulation in eastern France. The second step focuses on a daily synoptic classification derived from CAMS surface pressure fields, aiming to identify the dominant large-scale circulation regimes influencing Western Europe and, by extension, the study area in eastern France (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Block 1.b).\u003c/p\u003e\u003cp\u003eSpatial analysis was conducted through a k-means clustering applied to three meteorological variables common to both SAFRAN and CAMS datasets (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), in order to test the ability of CAMS to reproduce regional-scale structures over the study period. The optimal number of clusters was determined using the Elbow method (Syakur et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), which minimizes within-cluster variance based on Euclidean distances.\u003c/p\u003e\u003cp\u003eCirculation regimes were then derived from daily mean sea-level pressure fields, following approaches established in the literature (Davis et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Each day between 2015 and 2023 was classified into one of three circulation types: anticyclonic, intermediate, or cyclonic. This procedure provides a systematic assignment of daily regimes, allowing for the analysis of their temporal distribution and their influence on regional meteorological conditions.\u003c/p\u003e\u003cp\u003eBy providing a synthetic representation of synoptic-scale dynamics, this approach facilitates the exploration of links between large-scale circulation patterns and surface-level air quality. Following the identification of dominant circulation regimes, the framework is further extended by integrating in situ PM₂.₅ measurements, allowing a direct assessment of the influence of synoptic conditions on particulate pollution dynamics.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e2.2.2. Using circulation regimes to assess the local PM measurements\u003c/h2\u003e\u003cp\u003eDaily average concentrations of PM₂.₅ were extracted from the urban background stations of Dijon and Montb\u0026eacute;liard, covering the period from 1 January 2015 to 31 December 2022.\u003c/p\u003e\u003cp\u003eEach day was assigned to one of the previously defined circulation regimes (anticyclonic, cyclonic, or intermediate), enabling a systematic comparison between pollution levels and prevailing synoptic patterns at a daily time-scale.\u003c/p\u003e\u003cp\u003eThe objective is to assess the seasonal and interannual variability of PM₂.₅ concentrations under distinct circulation regimes. Two complementary approaches were adopted:\u003c/p\u003e\u003cp\u003e(1) a focus on the winter period (December\u0026ndash;January\u0026ndash;February, DJF), which is known to favor pollution episodes in the region studied in eastern France;\u003c/p\u003e\u003cp\u003e(2) An interannual perspective is also adopted to evaluate how frequently each circulation regime is associated with elevated or reduced pollution levels across different years.\u003c/p\u003e\u003cp\u003eTo support this analysis, the corresponding figures display, for each circulation regime, the distribution of daily PM₂.₅ concentrations during the DJF period over the eight-year study window. This provides a consistent framework for exploring both temporal variability and spatial contrasts in pollution response between two urban sites. To explore this topic further, we attempted to detect the number of atmospheric blockages, defined as \u0026ldquo;pollution episodes\u0026rdquo; where atmospheric pressure is equal to or higher than 1020 hPa over a period of more than five days (Ferreira et al., 2024).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e3.1. Climatological characterization and atmospheric regional dynamics based on CAMS (2015 \u0026ndash; 2023)\u003c/p\u003e\n\u003cp\u003eThe aim of this section is first to evaluate the performance of CAMS in reproducing the regional-scale structures produced by the high-resolution SAFRAN reanalysis and the seasonal variability of meteorological conditions. Subsequently, the dynamics of local influence on a fine scale can be approached. Two levels of analysis are proposed: (i)\u0026nbsp;the use of meteorological parameters to test spatialization based on CAMS and (ii) the use of pressure levels and their advantage in the classification of circulation regimes.\u003c/p\u003e\n\u003cp\u003e3.1.1. Evaluating Spatial Patterns in SAFRAN and CAMS Datasets through Cluster Analysis\u003c/p\u003e\n\u003cp\u003eIn Figure 2, the spatial patterns of CAMS and SAFRAN are in agreement. The lower resolution results in a substantially smaller number of grid points across the study domain (18 for CAMS versus 751 for SAFRAN). As summarized in Table 2, the clusters obtained from CAMS exhibit internal characteristics (relative humidity, temperature, and wind speed) broadly consistent with those identified using SAFRAN. Differences between the two datasets are systematic: CAMS clusters are associated with slightly higher mean temperatures (+0.5 to +0.9 \u0026deg;C) and wind speeds (+0.3 to +0.5 m s⁻\u0026sup1;), while relative humidity differences generally remain within \u0026plusmn;3 %.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 2\u003c/em\u003e\u003c/strong\u003e\u003cem\u003eAverage values of weather parameters used in SAFRAN and CAMS clustering (in bold) where RH (relative humidity, in %), t2m (temperature at 2 meters, in \u0026deg;C), VT (wind speed, in m/s). Liquid precipitation (RR) is not available in CAMS\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"605\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 230px;\"\u003e\n \u003cp\u003eSAFRAN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCAMS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eNumber of grid point\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003et2m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eVT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eRR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of grid point\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et2m\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e84.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e6.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e3.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e2.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e82.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e6.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e3.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e82.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e4.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e2.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e3.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e82.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e4.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e2.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e83.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e6.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e2.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e2.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e80.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e6.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e3.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e91.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e1.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eFour clusters are identified :\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eCluster 1 (blue, fig. 2) covers lowland areas (0\u0026ndash;400 m) with open topography, warmer conditions (6.94\u0026deg;C), and higher wind speeds (3.58m/s).\u003c/li\u003e\n \u003cli\u003eCluster 2 (red, fig. 2) corresponds to a small number of high-altitude mountainous pixels characterized by cold (4.89\u0026deg;C), humid (82.8%), and stable winter conditions.\u003c/li\u003e\n \u003cli\u003eCluster 3 (purple, fig. 2) is associated with enclosed valleys, including parts of the Doubs basin, less humid (80.1%), well ventilated (3.58m/s).\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eCluster 4 (green, fig. 2) corresponds to a high-altitude zone at the French\u0026ndash;Swiss border. Its characteristics largely reflect the spatial resolution of the CAMS grid; this cluster is therefore excluded from subsequent analyses.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eCluster 1, corresponding to lowland areas including Dijon, shows consistently warmer and windier conditions in CAMS than in SAFRAN (Tmean: 6.01 \u0026deg;C vs. 6.94 \u0026deg;C; wind speed: 3.08 m s⁻\u0026sup1; vs. 3.58 m s⁻\u0026sup1;), while Cluster 3, associated with valley environments, exhibits intermediate values for all three variables. Cluster 2 groups high-altitude areas such as parts of the Jura and the Alps, characterized by colder and more humid conditions in both datasets (Tmean: 4.34 \u0026deg;C [SAFRAN] vs. 1.25 \u0026deg;C [CAMS]; RH: 82.8 % vs. 91.3 %).\u003c/p\u003e\n\u003cp\u003eCluster 4, represented by a single CAMS pixel at the French\u0026ndash;Swiss border, displays very high relative humidity (91.3 %) and low mean temperature (1.25 \u0026deg;C), corresponding to high-altitude or fog-prone winter regimes. For these reasons, it was excluded from further analysis to prevent extreme or highly localized conditions from biasing the assessment of the main urban and valley environments.\u003c/p\u003e\n\u003cp\u003eThe two urban sites are distinctly represented: Dijon consistently falls within Cluster 1 in both datasets, while Montb\u0026eacute;liard is assigned to Cluster 3 in SAFRAN and to Cluster 2 in CAMS. The distribution of pixels across the three selected clusters highlights differences in spatial granularity between the datasets: in CAMS, Cluster 1 covers 8 pixels (44 %), Cluster 2 3 pixels (17 %), Cluster 3 6 pixels (33 %), and Cluster 4 a single pixel (6 %). In comparison, SAFRAN assigns 321 pixels (43 %) to Cluster 1, 143 pixels (19 %) to Cluster 2, and 287 pixels (38 %) to Cluster 3.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA comparative analysis of the spatial clustering results obtained from CAMS meteorological data and from the SAFRAN-based classification of Marion et al. (2025) indicates that CAMS produces regional structures comparable to those derived from SAFRAN (fig. 2), despite its coarser spatial resolution (~80 km compared to 8 km).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3.1.2. Atmospheric pressure: a key indicator of regional dynamics\u003c/p\u003e\n\u003cp\u003eTo complement the spatial clustering of weather parameters, a temporal dimension was added by classifying the daily evolution of sea-level atmospheric pressure over the period 2015\u0026ndash;2023 using CAMS data. Based on daily mean pressure in eastern France (including the 2 urban sites Dijon and Montb\u0026eacute;liard), three circulation regimes were identified (fig. 3):\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eLow-pressure regime (pressure \u0026lt; 1010 hPa, dark blue),\u003c/li\u003e\n \u003cli\u003eIntermediate regime (pressure between 1010 and 1020 hPa, light blue),\u003c/li\u003e\n \u003cli\u003eHigh-pressure regime (pressure \u0026gt; 1020 hPa, orange).\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe day-by-day classification was performed for each day over the entire period 2015\u0026ndash;2023, but the analysis presented here focuses specifically on the year 2020.\u003c/p\u003e\n\u003cp\u003eFigure 3.a shows the daily evolution of mean regional pressure in 2020, with the black curve representing the pressure and the coloured bands indicating the assigned regime for each day. In 2020, low-pressure regimes accounted for 16.2 % of days, intermediate regimes for 55.9 %, and high-pressure regimes for 27.9 %.\u003c/p\u003e\n\u003cp\u003eFigure 3.b presents the distribution of meteorological variables (relative humidity, mean temperature, wind speed) during the particulate season in 2020 for each regime. Anticyclonic conditions are associated with lower wind speeds (~2.5 m s⁻\u0026sup1;), whereas both low- and intermediate-pressure regimes display higher values. Figure 3.c details these variables separately for Dijon and Montb\u0026eacute;liard, showing spatial differences between the two urban sites. These contrasts illustrate the local heterogeneity of meteorological conditions within the study region.\u003c/p\u003e\n\u003cp\u003eThus, while CAMS reproduces well the major regional meteorological dynamics despite coarser spatial resolution, its real added value lies in the availability of daily sea-level pressure (not available in SAFRAN). This parameter enables a quantitative and robust day-to-day characterization of atmospheric circulation regimes, enhancing the ability to link these regimes to particulate pollution dynamics in the region. The following analysis therefore focuses on the impact of these regimes on in situ particulate matter pollution measurements.\u003c/p\u003e\n\u003cp\u003e3.2. CAMS as an integrated tool for meteorological and air quality diagnostics\u003c/p\u003e\n\u003cp\u003eThis section evaluates CAMS as an integrated tool for characterizing regional atmospheric dynamics via sea-level pressure and assessing urban-scale particulate pollution. The regime-based analysis confirms that CAMS reliably captures dominant circulation structures and enables the definition of meaningful meteorological regimes throughout the particulate season, particularly during the peak winter months (DJF: December \u0026ndash; January \u0026ndash; February).\u003c/p\u003e\n\u003cp\u003eWe further explore how these synoptic regimes influence in situ PM₂.₅ concentrations and evaluate CAMS\u0026rsquo;s ability to capture spatial contrasts between two nearby urban stations with distinct local environments, highlighting its potential to link synoptic-scale dynamics with localized pollution patterns.\u003c/p\u003e\n\u003cp\u003eThe analysis of average PM₂.₅ concentrations measured at the Montb\u0026eacute;liard (dotted line in Fig. 4) and Dijon (solid line in Fig. 4) stations covers the period 2015\u0026ndash;2022 and presents the mean over eight years. A marked seasonality of PM₂.₅ is observed, with the highest concentrations occurring during winter (DJF), coinciding with the heart of the particulate season in eastern France (Fig. 4).\u003c/p\u003e\n\u003cp\u003eAnticyclonic conditions (pressure \u0026gt; 1020 hPa) are associated with the highest average concentrations: Montb\u0026eacute;liard 19.0 \u0026plusmn; 5.11 \u0026micro;g/m\u0026sup3; (min: 12.6; max: 29.1) and Dijon 14.7 \u0026plusmn; 4.07 \u0026micro;g/m\u0026sup3; (min: 10.4; max: 23.3) (Table 2). Intermediate conditions yield moderate values (Montb\u0026eacute;liard 16.5 \u0026plusmn; 4.92 \u0026micro;g/m\u0026sup3;; Dijon 12.8 \u0026plusmn; 4.66 \u0026micro;g/m\u0026sup3;), while low-pressure regimes (\u0026lt; 1010 hPa) show the lowest averages (Montb\u0026eacute;liard 10.3 \u0026plusmn; 2.16 \u0026micro;g/m\u0026sup3;; Dijon 7.84 \u0026plusmn; 2.96 \u0026micro;g/m\u0026sup3;).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 3\u003c/em\u003e\u003c/strong\u003e\u003cem\u003eStatistics of PM\u003csub\u003e2.5\u0026nbsp;\u003c/sub\u003econcentrations measured at the stations (Dijon and Montb\u0026eacute;liard) according to Circulation regimes for all winters (DJF) over the period 2015\u0026ndash;2023\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"604\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eStation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003eAnticyclonic (A)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eIntermediate (I)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eLow pressure (L)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 123px;\"\u003e\n \u003cp\u003eDijon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e14.7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e12.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e7.84\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eStDev\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e4.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e4.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e2.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e23.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e20.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e10.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e7.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e3.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 123px;\"\u003e\n \u003cp\u003eMontb\u0026eacute;liard\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e19\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e16.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e10.3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eStDev\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e5.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e4.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e2.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e29.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e24.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e14.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e12.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e8.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e7.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAcross all circulation regimes, Montb\u0026eacute;liard records higher mean concentrations than Dijon. Seasonal contrasts are strongest in winter, when anticyclonic conditions are most frequent, with an average difference of +4.3 \u0026micro;g/m\u0026sup3; between the two stations. The concentration range (min\u0026ndash;max) is also larger in Montb\u0026eacute;liard than in Dijon, indicating greater variability at this site, particularly under anticyclonic conditions.\u003c/p\u003e\n\u003cp\u003eTo investigate this study further, it is necessary to consider these observations in a broader temporal context in order to assess the influence of synoptic patterns on PM\u003csub\u003e2.5\u003c/sub\u003e seasonal and interannual variability.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3.2.2. Seasonal and interannual variability of PM₂.₅ under synoptic regimes: comparing two urban sites\u003c/p\u003e\n\u003cp\u003eThe relationship between the frequency of synoptic weather systems and average PM₂.₅ concentrations is shown in Figure 5 for Montb\u0026eacute;liard and Dijon.\u003c/p\u003e\n\u003cp\u003eIn Montb\u0026eacute;liard, anticyclonic conditions show a strong positive correlation with PM₂.₅ concentrations (r = 0.90; p = 0.002) Intermediate conditions (r = \u0026ndash;0.26; p = 0.50) and low-pressure systems (r = 0.50; p = 0.216) exhibit no significant correlation.\u003c/p\u003e\n\u003cp\u003eIn Dijon, intermediate weather systems show a negative correlation with PM₂.₅ (r = \u0026ndash;0.75; p = 0.019), while low-pressure systems show a positive correlation (r = 0.78; p = 0.023). The correlation with high-pressure systems is moderate and borderline significant (r = 0.66; p = 0.051).\u003c/p\u003e\n\u003cp\u003eOverall, the analysis indicates that the relationship between synoptic regimes and PM₂.₅ concentrations is more pronounced at Dijon than at Montb\u0026eacute;liard. At both sites, anticyclonic (high-pressure) conditions consistently show the strongest positive correlations with PM₂.₅, highlighting the key role of stagnant, high-pressure weather in promoting pollution accumulation. The weaker or non-significant correlations under intermediate or low-pressure systems may reflect the influence of local topography, valley effects, and differing emission sources that modulate the response of particulate concentrations to synoptic conditions.\u003c/p\u003e\n\u003cp\u003eA strong working hypothesis is that atmospheric blocking events, characterized by persistent high-pressure systems, are likely to drive prolonged episodes of elevated PM₂.₅, although the magnitude and spatial extent of the effect may vary depending on urban morphology, ventilation patterns, and seasonal emission profiles. Quantifying this influence remains a priority for improving the predictive understanding of pollution episodes in contrasting urban environments.\u003c/p\u003e\n\u003cp\u003eThe scatter points in Figure 6 indicate several atypical winters that deviate from general trends, highlighting a case-study perspective on the interplay between synoptic regimes and local PM₂.₅ concentrations. Specific years identified include DJF 2015\u0026ndash;2016, DJF 2016\u0026ndash;2017, DJF 2018\u0026ndash;2019, and DJF 2019\u0026ndash;2020. During DJF 2016\u0026ndash;2017 and DJF 2015\u0026ndash;2016, PM₂.₅ concentrations reached up to ~28\u0026ndash;30 \u0026micro;g/m\u0026sup3;, coinciding with high anticyclonic frequency, while DJF 2019\u0026ndash;2020 exhibited lower PM₂.₅ levels alongside a lower frequency of anticyclones. For atmospheric blockages (defined as \u0026gt;1025 hPa for more than five consecutive days), anticyclonic conditions were associated with more than 50 blockage days, average seasonal wind speeds of 1.84 m/s, and average temperatures of \u0026ndash;0.50 \u0026deg;C. In Dijon, DJF 2016\u0026ndash;2017 was characterized by lower temperatures (~1 \u0026deg;C) and PM₂.₅ concentrations above the general trend. Under low-pressure conditions, DJF 2018\u0026ndash;2019 and DJF 2015\u0026ndash;2016 at PEJ exhibited higher PM₂.₅ levels, with average wind speeds of ~5.08 m/s and blockages persisting for 50 days. At LEV, PM₂.₅ remained elevated during DJF 2016\u0026ndash;2017 despite average low-pressure frequency. Overall, these observations illustrate that while high-pressure anticyclonic blockages generally drive elevated PM₂.₅ concentrations, local factors such as wind speed, temperature, and topography can modulate deviations from the general seasonal and interannual trends.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e4.1. Spatialization of Regional Weather Structures\u003c/h2\u003e\u003cp\u003eOur comparison of CAMS and SAFRAN classifications shows that, despite its coarser horizontal resolution (~\u0026thinsp;80 km versus ~\u0026thinsp;8 km for SAFRAN), CAMS reproduces the main regional meteorological structures over eastern France (Bourgogne Franche Comt\u0026eacute;). High-altitude areas (Cluster 2) systematically emerge as colder and more humid, while lowlands (Cluster 1) remain warmer and windier, and valleys (Cluster 3) display intermediate conditions. These results are consistent with previous validations of reanalysis products against fine-resolution observations. For instance, ERA5 has been shown to capture European wind speed climatologies with high accuracy compared to dense observational networks (Molina et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Similarly, CAMS global reanalysis has demonstrated skill in reproducing surface PM₂.₅ concentrations in complex environments such as the S\u0026atilde;o Paulo metropolitan area, with correlations ranging from 0.75 to 0.89 (Damascena et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe smoothing of microclimatic features, such as the Morvan hills or Jura subregions, reflects the intrinsic limitations of coarse-resolution reanalyses, as already noted in comparisons between global reanalyses and high-resolution regional models (e.g., ERA5 vs. SAFRAN; (Quintana-Segu\u0026iacute; et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2008\u003c/span\u003e)) This underlines that downscaling or high-resolution models remain essential for intra-urban or local-scale analyses. Nevertheless, CAMS captures the dominant spatial contrasts robustly, providing a reliable basis for regional-scale meteorological diagnostics.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e4.2. Sea-Level pressure classification\u003c/h2\u003e\u003cp\u003eThe use of sea-level pressure at a 3-hourly temporal resolution provides a quantitative and reproducible framework for defining circulation regimes, reducing the subjectivity inherent in traditional synoptic classifications such as the Groβwetterlagen (GWL) approach (Philipp et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Trigo \u0026amp; DaCamara, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Unlike conventional indices that rely on daily averages or subjective pattern recognition, the high temporal resolution of CAMS enables the rapid evolution of synoptic structures to be tracked, offering a finer representation of intraday variability. This is particularly relevant for diagnosing pollution episodes, which often respond to transient circulation features that may be overlooked in lower-resolution datasets.\u003c/p\u003e\u003cp\u003eA key result of our analysis is the identification of an intermediate pressure regime, bridging the conventional binary distinction between anticyclonic (\u0026gt;\u0026thinsp;1020 hPa) and low-pressure conditions. Transitional regimes were especially frequent in spring and autumn, when moderate stability dominates. The relevance of such intermediate states has been emphasized in recent research: Cattiaux et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) showed that variations in the sinuosity of midlatitude atmospheric flows modulate the persistence of surface weather regimes, directly impacting pollutant accumulation. Similarly, Huang et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) demonstrated that intermediate circulation states and associated wind variability strongly control pollutant dispersion in urban street canyons, highlighting the need to move beyond binary classifications.\u003c/p\u003e\u003cp\u003eIn the 2020 dataset, intermediate regimes predominated (55.9% of days), with anticyclonic conditions representing 27.9% and low-pressure systems 16.2%. This confirms that including transitional regimes provides a more complete and realistic depiction of regional atmospheric dynamics.\u003c/p\u003e\u003cp\u003eThe contrast observed between Dijon and Montb\u0026eacute;liard illustrates how local heterogeneities (topography, valley orientation, land use) modulate the regional circulation imprint. For instance, Montb\u0026eacute;liard exhibited higher humidity and more frequent stagnation episodes, favoring PM₂.₅ accumulation. Comparable results were reported by Zhu et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) in the Eastern Monsoon Region of China, where interactions between synoptic weather types and complex terrain amplified local contrasts in PM₂.₅ levels.\u003c/p\u003e\u003cp\u003eOverall, these results demonstrate that CAMS, by providing high-frequency sea-level pressure data, enables a robust and reproducible classification of circulation regimes. This approach improves our understanding of the meteorological drivers of particulate pollution and holds potential for operational applications in air quality management.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e4.3. PM\u003csub\u003e2.5\u003c/sub\u003e response to synoptic regimes\u003c/h2\u003e\u003cp\u003eConsistent with previous research, our results show that PM₂.₅ concentrations are systematically highest under anticyclonic regimes than in the 2 other regimes, confirming the role of atmospheric stability and suppressed vertical mixing in promoting particle accumulation. These conditions are particularly critical in winter, when thermal inversions, low wind speeds, and reduced turbulence favor pollutant persistence in the lower atmosphere. For example, Largeron \u0026amp; Staquet (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) demonstrated that persistent thermal inversions in Alpine valleys (Grenoble area) strongly enhance PM₁₀ accumulation during winter pollution episodes. Similar findings across Europe highlight how stable high-pressure systems amplify stagnation conditions and pollutant build-up (Pearce et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Vautard et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). In contrast, low-pressure regimes are consistently associated with lower PM₂.₅ levels, reflecting enhanced ventilation, stronger turbulence, and more efficient dispersion. This dichotomy between high- and low-pressure systems reinforces the concept that synoptic circulation patterns are a primary driver of seasonal and episodic variations in fine particulate matter, in line with studies linking large-scale meteorology to urban air quality (Leung et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Pope et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eA particularly noteworthy result of our analysis is the role of the intermediate pressure regime. While its meteorological structure suggests moderate stability, it exhibits variable impacts on PM₂.₅ concentrations. This highlights the potential of such transitional states to capture nuanced conditions \u0026ndash; especially in spring and autumn \u0026ndash; that are often overlooked by binary classifications. It also raises the prospect that this intermediate regime may provide a valuable diagnostic tool for identifying pollution-prone conditions beyond the simplistic high/low-pressure distinction.\u003c/p\u003e\u003cp\u003eThe analysis of in situ PM₂.₅ data clearly reflects the CAMS-derived circulation regimes. Prolonged anticyclonic events (\u0026gt;\u0026thinsp;1020 hPa sustained for \u0026ge;\u0026thinsp;5 days) correspond to the highest concentrations observed, demonstrating that the persistence of stable high-pressure systems is a key determinant of extreme pollution episodes. Conversely, depressionary conditions (\u0026lt;\u0026thinsp;1010 hPa) systematically enhance particle dispersion, underlining the importance of regime type, duration, and seasonality in shaping air quality.\u003c/p\u003e\u003cp\u003eTogether, these results demonstrate that coupling synoptic-scale classifications from CAMS with surface observations provides a robust framework for diagnosing and predicting air pollution episodes. Beyond its relevance for northeast France, this approach is potentially transferable to other regions, particularly where observational networks are sparse and reliance on reanalyses is critical for air quality management.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e4.4. Local contrast at urban scale\u003c/h2\u003e\u003cp\u003eDespite the robust relationship between synoptic-scale circulation and PM₂.₅ described above, the observed interannual variability demonstrates that meteorology alone cannot fully explain fluctuations in particulate concentrations. As widely documented, the proximity of local emission sources \u0026ndash; including traffic, residential heating, and industry \u0026ndash; remains decisive in shaping both baseline levels and pollution peaks. For example, study in Eastern Europe by Juda-Rezler et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) identified residential combustion (46%) and traffic emissions (~\u0026thinsp;31%) as dominant contributors to PM₂.₅, with significant seasonal variability \u0026ndash; highlighting the interplay between local sources and meteorological drivers. Similarly, residential heating is a dominant contributor in winter, accounting for up to 80\u0026ndash;90% of PM in some Slovakian cities, particularly during cold spells and stable conditions (Jandacka et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In heavily industrialized regions such as the Beijing\u0026ndash;Tianjin\u0026ndash;Hebei (BTH) megacity cluster, industrial sources are responsible for nearly one-third of PM₂.₅, with peak contributions in autumn and winter when stagnant synoptic patterns favor accumulation (Zeng et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These examples confirm that traffic, heating, and industrial emissions are critical determinants of urban PM₂.₅, with their influence strongly modulated by seasonal meteorology.\u003c/p\u003e\u003cp\u003eThis limitation reflects the coarse spatial resolution of CAMS (~\u0026thinsp;80 km) and the absence of detailed surface data assimilation, which constrain its ability to capture intra-urban contrasts. Previous evaluations have shown similar limitations for global reanalyses when compared with high-resolution regional models (Quintana-Segu\u0026iacute; et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe two urban sites analyzed, Montb\u0026eacute;liard and Dijon, exemplify this scale mismatch. Montb\u0026eacute;liard consistently exhibits higher mean concentrations and greater variability across all synoptic regimes, especially during winter anticyclonic episodes. By contrast, Dijon, located in a more open topographic setting, shows lower and more stable levels. These differences are consistent with topographic influences: valley confinement and reduced ventilation in Montb\u0026eacute;liard favor pollutant accumulation, while Dijon benefits from enhanced mixing. Similar findings have been reported in Alpine and Central European valleys, where terrain-induced stagnation amplifies pollution episodes under synoptic blocking. For example, Rodrı́guez et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) illustrated this effect in Southern and Eastern Spain, showing how topography and the transport of Saharan dust interact with stagnant atmospheric conditions to increase PM\u003csub\u003e10\u003c/sub\u003e concentrations.\u003c/p\u003e\u003cp\u003eSuch site-specific variability underscores that meteorology alone cannot account for interannual fluctuations in PM₂.₅. Instead, local emissions, topography, and regional circulation interact to determine pollution levels. The analysis of atypical winters \u0026ndash; characterized by persistent blocking anticyclones lasting\u0026thinsp;\u0026ge;\u0026thinsp;5 days \u0026ndash; further illustrates this interplay: extreme pollution events occur when synoptic-scale stagnation coincides with local conditions favorable to accumulation.\u003c/p\u003e\u003cp\u003eOverall, these findings highlight the need to integrate high-resolution emission inventories and local-scale modeling (e.g., SIRANE, ADMS-Urban) with CAMS-derived synoptic regimes. Such a multi-scale approach would improve the understanding of wintertime pollution episodes and provide more operationally relevant tools for urban air quality management.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eOur results show that CAMS provides a robust representation of regional synoptic patterns, reproducing the dominant spatial structures observed in higher-resolution SAFRAN datasets. Despite its coarse resolution (~\u0026thinsp;80 km), CAMS captures the key contrasts between highlands, lowlands, and valleys in Bourgogne Franche Comt\u0026eacute;, confirming its utility for regional meteorological diagnostics. The availability of high-temporal-resolution sea-level pressure data allows a quantitative and reproducible day-by-day classification of circulation regimes. Anticyclonic conditions are associated with elevated PM₂.₅ through enhanced atmospheric stability and reduced ventilation, whereas low-pressure systems favor pollutant dispersion\u0026mdash;patterns fully consistent with previous studies.\u003c/p\u003e\u003cp\u003eImportantly, our analysis highlights an intermediate circulation regime, which exhibits distinct PM₂.₅ behavior and demonstrates that daily synoptic classification can provide insights beyond the conventional high- and low-pressure extremes.\u003c/p\u003e\u003cp\u003eThe study also shows that meteorology alone cannot explain all interannual variability of PM₂.₅, emphasizing the critical role of local emission sources and topographic effects. Coarse-resolution reanalyses like CAMS are therefore insufficient for street-level assessments, where sub-kilometer heterogeneities must be resolved with high-resolution urban models (e.g., SIRANE).\u003c/p\u003e\u003cp\u003eTaken together, these results demonstrate that CAMS offers a coherent synoptic-scale framework for analyzing, forecasting, and managing wintertime particulate pollution episodes. Its ability to link circulation regimes to PM₂.₅ variability provides operationally relevant information for air quality management, while the methodology can be transposed and scaled to other regions with limited observational networks. In combination with local emission inventories and fine-scale dispersion models, CAMS thus constitutes a powerful tool for integrated environmental and urban air quality studies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eS.M wrote the main manuscript text. N.M and P.R have reviewed the document, making amendments and providing recommendations. All authors have reviewed the manuscript and approved its final version.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eArtı́\u0026ntilde;ano, B., Salvador, P., Alonso, D. G., Querol, X., \u0026amp; Alastuey, A. (2003). 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The Relationships between PM\u003csub\u003e2.5\u0026nbsp;\u003c/sub\u003eand Meteorological Factors in China : Seasonal and Regional Variations. \u003cem\u003eInternational Journal of Environmental Research and Public Health\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(12), 1510. https://doi.org/10.3390/ijerph14121510\u003c/li\u003e\n \u003cli\u003eZeng, Y., Sui, X., Ma, C., Liao, R., Yang, J., Wang, D., \u0026amp; Zhang, P. (2024). Industrial Heat Source-Related PM\u003csub\u003e2.5\u003c/sub\u003e Concentration Estimates and Analysis Using New Three-Stage Model in the Beijing\u0026ndash;Tianjin\u0026ndash;Hebei Region. \u003cem\u003eAtmosphere\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(1), 131. https://doi.org/10.3390/atmos15010131\u003c/li\u003e\n \u003cli\u003eZhu, S., Wang, Z., Qu, K., Xu, J., Zhang, J., Yang, H., Wang, W., Sui, X., Wei, M., \u0026amp; Liu, H. (2023). Spatial Characteristics and Influence of Topography and Synoptic Systems on PM\u003csub\u003e2.5\u003c/sub\u003e in the Eastern Monsoon Region of China. \u003cem\u003eAerosol and Air Quality Research\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e(10), 220393. https://doi.org/10.4209/aaqr.220393\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7564554/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7564554/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAir pollution episodes involving fine particulate matter (PM₂.₅) are tightly linked to synoptic meteorology, which regulates accumulation and dispersion. This study evaluates the ability of Copernicus Atmosphere Monitoring Service (CAMS) reanalyses (2015\u0026ndash;2023) to support a daily-scale classification of circulation regimes relevant for air quality in eastern France. CAMS near-surface parameters (temperature, relative humidity, wind) were compared with the high-resolution SAFRAN reanalyses, and CAMS sea-level pressure fields were used to derive a reproducible classification benchmarked against Gro\u0026szlig;wetterlagen.\u003c/p\u003e\u003cp\u003eThe present study highlights three main regimes. Anticyclonic situations promote strong PM₂.₅ accumulation under stable, poorly ventilated conditions. Low-pressure regimes enhance dispersion through stronger winds and mixing, limiting concentrations. An intermediate regime, less documented in previous classifications, combines moderate pressure gradients and variable transport pathways, producing heterogeneous pollution levels and occasional long-range particle transport.\u003c/p\u003e\u003cp\u003eResults show good climatological agreement between CAMS and SAFRAN, with CAMS reproduces the main meteorological and synoptic patterns, while smoothing finer-scale contrasts. The classification explains both seasonal patterns and interannual variability, while underlining the persistent contribution of local emissions (traffic, heating, industry).\u003c/p\u003e\u003cp\u003eOverall, CAMS provides a robust synoptic-scale framework for meteorological typologies relevant to air quality. Although its coarse resolution constrains intra-urban representation, coupling with high-resolution urban models could substantially enhance the diagnosis, forecasting, and management of particulate pollution episodes. This approach would not only improve the characterization of wintertime events but also capture the broader annual particle season, thereby providing more robust support for the development of effective mitigation strategies.\u003c/p\u003e","manuscriptTitle":"CAMS products for analyzing atmospheric dynamics to develop QA indicators at regional scale","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-10 13:48:19","doi":"10.21203/rs.3.rs-7564554/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"613b6c4d-3cba-43c7-9300-2405fed02570","owner":[],"postedDate":"October 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-15T15:09:29+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-10 13:48:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7564554","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7564554","identity":"rs-7564554","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}